There are a number of conventional methods for detecting abnormalities in a brain from a study of neuroimages, such as those obtained using magnetic resonance imaging (MRI) techniques. One conventional method is based on the analysis of tissue classes and another conventional method is based on the analysis of the symmetry between the two hemispheres of the brain by, for example, extracting structures, or finding point-to-point inter-hemispheric correspondence and cross-correlation.
A number of papers have been published describing the analysis of tissue classes, for example, the paper entitled Validity Of Guided Clustering For Brain Tumour Segmentation, by Velthuizen, R. P., published by the Engineering in Medicine and Biology Society, 1995, IEEE 17th Annual Conference, V. 1, 1995, 413-414. In this paper, a method is described which detects abnormalities in the brain by allowing the validity of small classes, like tumours, to have a noticeable effect on the validity measure. However, only three tumour data sets, and no normal data sets were tested and reported in this publication.
Unsupervised Segmentation For Automatic Detection Of Brain Tumours In MRI, by Capelle A. S., Alata O., Fernandez C., Lefevre S., Ferrie J. C., published as Proceedings of International Conference on Image Processing IPMI 2000, V. 1, 2000, 613-616 describes a multiple resolution segmentation method in which the brain is divided into homogeneous Gaussian distributed classes. In this method, the maximum a posteriori method is used to estimate the parameters of each class to detect the existence of tumours in a two-dimensional MR image. This method is based on the segmentation of tumours using knowledge of the intensity distribution. However, due to the overlapping intensity of tumour(s) with other normal tissues, this method may segment the tissues incorrectly. This iterative method may also be time consuming although no indication is given in this paper of the run time.
A number of papers have been published describing the analysis of the symmetry between the two hemispheres of the brain. Human brains exhibit an approximate bilateral symmetry with respect to the inter-hemispheric (longitudinal) fissure bisecting the brain, known as the mid-saggital plane (MSP). These methods are based on the assumption that a healthy human brain is roughly symmetrical and an abnormality may be detected from brain asymmetry. A common way of detecting abnormalities using such a method is to consider local geometrical asymmetries, such as changes in the relative shape and structure of the hemispheres. Such a method is described in the paper entitled Cortical Variability and Asymmetry in Normal Ageing and Alzheimer's Disease, by Thompson P M, Moussai J, Zohoori S, Goldkorn A, Khan A A, Mega M S, Small G W, Cummings J L, Toga A W which was published in Cereb. Cortex. 1998 September; 8(6):492-509].
In a paper entitled Knowledge-based Classification And Tissue Labelling Of MR Images Of Human Brain Medical Imaging, by Chunlin Li; Goldgof, D. B.; Hall, L. O. which was published in IEEE Transactions, 1993, Vol. 12(4), 740-750, it is suggested that, using a knowledge based approach involving an estimation of the symmetry of cerebro-spinal fluid (CSF), a tumour can be detected only in the slices containing CSF. The measures used are based strictly on predefined intensity thresholds which can vary from one data set to another. It was assumed that the tumors appear to have intensity higher than that of grey matter on T2-weighted images.
A development of the knowledge-based approach described in the above-mentioned paper was published in a paper entitled MR Brain Image Segmentation Using Fuzzy Clustering, by Ock-Kyung Yoon; Dong-Min Kwak, Dong-Whee Kim, KilHoum Park and published as IEEE International Fuzzy Systems Conference Proceedings, 1999, FUZZ-IEEE '99, Volume: 2, 853-857. In this paper a method is described where fuzzy c-means are used in slices containing CSF to separate grey matter, white matter, and CSF in the cerebrum. In this method, a symmetrical measure based on the number of pixels, moment invariants, and Fourier descriptors is described as being used to quantify the normality of image slices of the brain being studied. However, this algorithm has a number of disadvantages, for example, the quantification of normality is based only on 40 slices in 1 normal and 2 abnormal T2-weighted studies, also, as the symmetrical measure uses a large number of user defined parameters, which are difficult to estimate.
In the publication by Joshi S, Lorenzen P, Gerig G, Bullitt E. entitled Structural and radiometric asymmetry in brain images, Med Image Anal. 2003 June; 7(2):155-170, structural and radiometric asymmetry was analysed through large deformation image warping in three dimensions. Nine tumour and four normal cases were tested, however, there is no information given on the running time. The second stage of the algorithm described in this paper is based on a Christensen warping algorithm which has an extremely long run time and is described in the paper by Christensen G E, R D Rabbit, M I Miller entitled Deformable Templates Using Large Deformation Kinematics, IEEE Transactions on Image Processing, 5(10), 1996, pp. 1435-1447.
All the conventional methods mentioned above, with the exception of that described in the publication by Joshi S, Lorenzen P, Gerig G, Bullitt E. entitled Structural and radiometric asymmetry in brain images, Med Image Anal. 2003 June; 7(2):155-170, work in two dimensions.